AN INTEGRATED NETWORK ANALYSIS OF PSORIASIS: A NOVEL APPROACH TO DISEASE PATHOLOGY

Authors

  • Alex Anand D
  • Harishchander A
  • Jason Ub

Abstract

Objective: Psoriasis is a chronic autoimmune disorder. At present, about 2% of human population is affected by psoriasis in a global scale.
There is no permanent cure for psoriasis in the post-genomic era and the disease mechanism too is poorly understood. We hereby investigate
psoriasis through a systems biology approach to identify the underlying regulatory networks, which are pivotal to the disease pathology of
psoriasis.
Methods: Initially, we surveyed microarray studies from array express, and then we extracted the list of implicated genes through array mining tools.
We then verified the nomenclature of extracted genes and extracted gene ontology information from various publications and databases such as UCSC,
HUGO, and DAVID. We then have identified the list of novel micro RNA (miRNAs), transcription factors and pathways, which are involved in the disease
pathology of psoriasis from EnrichR.
Results: EnrichR predicted 193 miRNAs, 183 transcription factors, and 116 pathways. After applying various mining techniques and statistics, we
identified a very few transcriptions factors and miRNAs, which are related to the disease pathways of psoriasis. Finally, we have used t-test to identify
a specific miRNA and transcription factors, which are associated with the disease pathology of psoriasis on the basis of pathway analysis and it was
identified that hsa-miR-324-5p and PAX3 have a higher degree of association on the basis of p-value.
Conclusion: Integrated network analysis of biological data is an exciting view point to view and understand the pathological conditions in a biological
system, but until date this field has not developed enough to encompass etiology and therapy. In order to take an equilibrium shift from the level of
disease understanding to pattern characterization and therapy, there is a requirement for conducting more experimental studies on human with the
respective ailments. At present, we have applied the approach of network analysis to psoriasis and in future we will be applying this approach to
understand the disease pathology of various disorders of autoimmune nature.

Keywords: Psoriasis, Micro RNA, Post-genomics, Bioinformatics and systems biology.

Downloads

Download data is not yet available.

References

Lomholt G. Prevalence of skin diseases in a population; a census study from the Faroe Islands. Dan Med Bull 1964;11:1-7.

Farber EM, Nall ML. Epidemiology: Natural history and genetics. In: Roenigk HH, Maibach HI, editors. Psoriasis. New York: Marcel Dekker; 1998. p. 107-58.

Kenney JA. Psoriasis in the American black. In: Farber EM, Cox AJ, editors. Psoriasis: Proceedings of the International Symposium, Stanford University. Stanford, California: Stanford University Press; 1971. p. 49-52.

Christophers E. Psoriasis – Epidemiology and clinical spectrum. Clin Exp Dermatol 2001;26(4):314-20.

Henseler T, Christophers E. Psoriasis of early and late onset: Characterization of two types of psoriasis vulgaris. J Am Acad Dermatol 1985;13(3):450-6.

Watson W, Cann HM, Farber EM, Nall ML. The genetics of psoriasis. Arch Dermatol 1972;105:197-207.

Altmüller J, Palmer LJ, Fischer G, Scherb H, Wjst M. Genomewide scans of complex human diseases: True linkage is hard to find. Am J Hum Genet 2001;69(5):936-50.

Davidson A, Diamond B. Autoimmune diseases. N Engl J Med 2001;345:340-50.

Lee RC, Feinbaum RL, Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993;75(4):843-54.

Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, Bettinger JC, Rougvie AE, et al. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 2000;403(6772):901-6.

Maragkakis M, Alexiou P, Papadopoulos GL, Reczko M, Dalamagas T, Giannopoulos G, et al. Accurate microRNA target prediction correlates with protein repression levels. BMC Bioinformatics 2009;10:295.

Lee RC, Ambros V. An extensive class of small RNAs in Caenorhabditis elegans. Science 2001;294(5543):862-4.

Baek D, Villén J, Shin C, Camargo FD, Gygi SP, Bartel DP. The impact of microRNAs on protein output. Nature 2008;455(7209):64-71.

Lytle JR, Yario TA, Steitz JA. Target mRNAs are repressed as efficiently by microRNA-binding sites in the 5’ UTR as in the 3’ UTR. Proc Natl Acad Sci U S A 2007;104(23):9667-72.

Mraz M, Pospisilova S. MicroRNAs in chronic lymphocytic leukemia: From causality to associations and back. Expert Rev Hematol 2012;5(6):579-81.

Zheng H, Fu R, Wang JT, Liu Q, Chen H, Jiang SW. Advances in the techniques for the prediction of microRNA targets. Int J Mol Sci 2013;14(4):8179-87.

Gan HH, Gunsalus KC. Tertiary structure-based analysis of microRNA-target interactions. RNA 2013;19(4):539-51.

Manczinger M, Kemény L. Novel factors in the pathogenesis of psoriasis and potential drug candidates are found with systems biology approach. PLoS One 2013;8(11):e80751.

Harishchander A, Anand DA. An insilico methodology for predicting novel micro RNAs with therapeutic significance. Int J Sci Eng Technol 2014;6:720-1.

Harishchander A, Anand DA. Computational approach for identifying therapeutic micro RNAs. Int J Pharm Pharm Sci 2014;6:638-40.

Harishchander A, Anand DA. Binding site analysis of micro RNAs target interaction from genome wide association studies. Asian J Pharm Clin Res 2014;7:121-2.

Harishchander A, Anand DA. A computational approach for identifying novel micro RNAs from genome wide association studies of psoriasis. Int J Innov Res Sci Eng 2014;2:288-91.

Harishchander A, Anand A. Computational analysis of micro RNA based target interactions related to genome wide association studies of psoriasis. J Pure Appl Microbiol 2014;8:823-6.

Roberts RJ. PubMed central: The Gen bank of the published literature. Proc Natl Acad Sci U S A 2001;98(2):381-2.

Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28(1):27-30.

Chen EY, Tan CM, Kou, Y, Duan, Q, Wang Z, Meirelles G, et al. Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 2013;14:128.

Published

01-05-2015

How to Cite

Anand D, A., Harishchander A, and Jason Ub. “AN INTEGRATED NETWORK ANALYSIS OF PSORIASIS: A NOVEL APPROACH TO DISEASE PATHOLOGY”. Asian Journal of Pharmaceutical and Clinical Research, vol. 8, no. 3, May 2015, pp. 176-8, https://mail.innovareacademics.in/journals/index.php/ajpcr/article/view/5244.

Issue

Section

Original Article(s)